Top Machine Learning Jobs in Toronto

The CTO will lead Fathom's technical strategy, focusing on AI governance, building partnerships, defining standards, and advancing AI safety practices.

As a Software Engineering Intern, you will contribute to building features for user-facing products, APIs, and scalable services for machine learning. Responsibilities include crafting code, designing data pipelines, implementing tools, and enhancing technical documentation.

Lead product strategy and roadmap for Coalition's claims experience, focusing on operational efficiency, automation, and platform migrations. Partner with engineering, design, data science, and cross-functional stakeholders to build tooling, drive system migrations, define requirements, and measure impact. Use data and AI/ML-powered insights to reduce manual work, increase claims capacity, and improve broker and policyholder outcomes while managing complex, cross-team initiatives.

The Senior Product Manager will drive the AI product roadmap for Revenue Cycle Management, overseeing product lifecycle and collaboration with various teams to enhance billing workflows with AI integration.

One-year, full-time fellowship supporting design, delivery, and evaluation of interventions for smallholder farmers. Duties include research and analysis, pilot design, field implementation, M&E, and cross-team collaboration with heavy mentorship and rotating in-country placements.

The Senior Principal Researcher will lead AI research initiatives, mentor teams, translate research into scalable systems, and represent Huawei in the global AI community.

Develop and commercialize firmware for neural network processing on ARM-based processors. Design embedded software for real-time processing, manage buffer interrupts, and optimize compilers.

Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.

Architect, implement, and maintain scalable, object-oriented test automation frameworks for UI and server-side microservices. Write production-grade automation code, integrate AI utilities for self-healing and predictive analytics, embed tests in CI/CD pipelines using container orchestration, validate APIs and databases, and collaborate with cross-functional teams to enable shift-left testing and rapid defect resolution.

Lead and scale end-to-end data annotation operations: set quality standards, build and manage an in-house annotation team and vendors, translate research into annotation guidelines, implement QA systems, drive AI-assisted labeling, partner with ML/CV and product, and own metrics and reporting.

Build, optimize, and embed machine learning models for on-device inference within a Go-based detection engine. Own models end-to-end: deploy, monitor performance, detect drift, iterate, and resolve production issues while collaborating with systems engineers and stakeholders.

The Technical Product Manager will lead product direction, interface with engineering teams, and ensure the successful deployment of AI-powered safety systems. Responsibilities include defining product vision, translating customer needs, and maintaining high product quality.

Lead and operationalize AI delivery across internal teams and client engagements: embed AI-enabled workflows, define playbooks and governance, mentor delivery teams, support pre-sales and solutioning, and drive production-grade AI solutions with measurable business outcomes for mid-market and enterprise clients.

Use legal training to create and evaluate AI training data and relevance evaluations. Perform legal research and analysis, provide actionable feedback, support senior team members, present to stakeholders, and advocate for Responsible AI.

Lead product strategy and roadmap for eBay Search, driving growth through AI-driven features. Partner with engineering, design, data science, and analytics to build MVPs, run A/B tests, iterate quickly, and measure impact to improve discovery, personalization, and user experience.

Lead commercial pipeline for quantum algorithms and applications: identify opportunities, pitch capabilities, convert to funded engagements, manage proposals, pricing, contracts, and oversee delivery while coordinating between research teams and external partners.

Design and run experiments to characterize OLED and related semiconductor devices, analyze and present data, operate and troubleshoot R&D equipment, collaborate with engineers, maintain lab safety, and generate test documentation to support materials development and production readiness.

Provide embedded legal counsel to product and engineering on privacy, IP, and consumer protection; lead Arena's global privacy program (GDPR, CCPA/CPRA, cross-border transfers); advise on AI governance and responsible deployment; support commercial contracts and data flow frameworks; build privacy processes, playbooks, internal agents, and legal-tech integrations to scale operations.

As a Co-op Researcher, you will engage in research and development in computer vision and machine learning, optimizing algorithms and models for mobile computing constraints.

The Co-op Researcher will research and develop cutting-edge technology in machine learning and computer vision, focusing on algorithm optimization for mobile devices.

In this role, the researcher will specialize in computer vision and machine learning, developing algorithms, evaluating solutions, and collaborating with teams.

Assist in research and development of computer vision and machine learning technologies through deep learning and image analysis. Optimize algorithms for mobile deployment and contribute to projects involving image and video analysis.

The role involves leading AI/ML technical planning, evaluating new technologies, collaborating with stakeholders, and fostering innovation in the team.

The researcher will develop and evaluate AI/ML models, prototype innovative solutions, and collaborate to enhance product capabilities for smart devices.

The AI/ML Engineer will design, develop, and optimize machine learning models for mobile applications, collaborating with teams to enhance user experiences and innovate in AI-driven solutions.